code: def test_pytorch_roundtrip_conversion():
      import torch
  
      xp_tensor = numpy.zeros((2, 3), dtype="f")
      torch_tensor = xp2torch(xp_tensor)
      assert isinstance(torch_tensor, torch.Tensor)
      new_xp_tensor = torch2xp(torch_tensor)
      assert numpy.array_equal(xp_tensor, new_xp_tensor)
  
file_path: thinc/thinc/tests/layers/test_pytorch_wrapper.py
imports: import numpy
  import pytest
  from thinc.api import SGD, ArgsKwargs, CupyOps, Linear, MPSOps, NumpyOps, PyTorchWrapper, PyTorchWrapper_v2, PyTorchWrapper_v3, Relu, chain, get_current_ops, torch2xp, use_ops, xp2torch
  from thinc.backends import context_pools
  from thinc.compat import has_cupy_gpu, has_torch, has_torch_amp, has_torch_mps_gpu
  from thinc.layers.pytorchwrapper import PyTorchWrapper_v3
  from thinc.shims.pytorch import default_deserialize_torch_model, default_serialize_torch_model
  from thinc.shims.pytorch_grad_scaler import PyTorchGradScaler
  from thinc.util import get_torch_default_device
  from ..util import check_input_converters, make_tempdir
label_desc: Use image node_id 5 for calling a global function with example usage: test_pytorch_roundtrip_conversion() without return types